<![CDATA[Corrupt behaviour by government officials often occurs and becomes a problem that can disturb the public and threaten the integrity of the government system. Social media has become an important means for the public to voice their opinions and sentiments on social issues, including corrupt behaviour by government officials. This study aims to analyze the corrupt behaviour of government officials based on public sentiment on social media using the Naïve Bayes Classifier method. Data was obtained from Twitter with keywords closely related to corruption cases involving government officials, data obtained in a certain period. The Naïve Bayes Classifier method was applied to classify tweets related to corrupt behaviour by government officials to later be categorized into positive sentiment, and negative sentiment. The results of this study conclude that the TF-IDF Weighting Process, 3 words are very dominant and often appear in public sentiments, namely the words "Corruption", "Official" and the word "Tax". This shows that the public is very angry and disappointed, and this results in a very low level of trust in corrupt behaviour carried out by government officials. Especially those carried out by tax officials]]>
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